基于MODIS数据的湖北省地级以上城市城区地表温度变化分析

基于MODIS数据,以湖北省地级以上城市城区为研究对象,通过对湖北省13个地级以上城市城区边界矢量数据与地表温度因子进行套合处理,采用叠置分析方法对2000、2015、2017年湖北省地级以上城市城区的地表温度进行统计,并分析城镇化建设对城区地表温度产生的影响。     

气候变化下基于物种与植物耦联机制的小白额雁(Anser erythropus)潜在生境及适宜性研究--以长江流域中下游为例

气候变化和人类活动通过改变物种生境而影响物种多样性。小白额雁是长江流域中下游的一种具有较高生态价值的食草型濒危候鸟,受气候变化和人类活动威胁。本文以小白额雁为代表性物种,定量分析了气候变化对长江流域中下游候鸟潜在生境及适宜性空间分布格局的影响。采用Maxent模型模拟了当前情景和全球环流模型(GCMs)气候场景下小白额雁潜在生境及其适宜性分布。研究结果表明,小白额雁分布特征与其栖息地周边植物分布呈显著相关关系;运用Maxent模型模拟小白额雁六种主要食源植物的分布特征,并将其结果作为环境变量,将显著改善小白额雁潜在生境及其适宜性模型的模拟性能;在两种典型浓度情景(RCP 2.6和RCP8.5)下,2070年小白额雁潜在生境适宜性面积将下降。为应对气候变化对小白额雁的影响,应采取更加合理的管理措施和保护政策,包括调整保护区的大小、形状和用途。     

利用MODIS红外资料反演大气参数以及表层温度的研究

提出了用牛顿非线性迭代法同时反演大气参数和表层温度,该反演算法应用到我国渤海地区MODIS红外资料中,可反演得到中尺度范围内的大气温度、水汽廓线,其误差分别不超过1.5 K和18%.反演得到的表层温度、大气可降水量(TPW)和大气稳定度(TTI)与美国国家宇航局(NASA)MOD07产品相似.     

基于地面采样点的MODIS等效反射率计算与分析

本文介绍了等效反射率、平均等效反射率的概念及计算方法,以2006年10月在崇明东滩湿地采集的地面光谱反射率数据和MOD IS地面反射率产品为基础数据,计算了试验区等效反射率和平均等效反射率,开展了平均等效反射率与MOD IS反演反射率的比较分析,对MOD IS地面反射率产品精度作了分析和评价。     

遥感技术在我国海冰研究方面的进展

本文首先对遥感技术在我国海冰研究中的发展情况作了详细地论述,并介绍了几种常用于海冰研究的遥感数据;其次,从海冰厚度识别、海冰运动速度矢量计算、海冰资源量测算以及海冰灾害监测等四个方面,简单论述了遥感技术在海冰研究中的应用。最后,以渤海海冰为例,阐述了遥感技术的具体应用,同时指出了今后的发展方向。     

基于MODIS资料反演海上能见度的经验模型

利用2002年10月~2003年8月的中分辨率成像光谱数据MODIS(ModerateResolutionImagingSpectrora-diometer)和华南沿海及海上能见度观测数据,对华南海域连续的、长时间序列的能见度遥感监测和定量反演进行了试验。将MODIS影像分为:晴空(Ⅰ);雾和低云(Ⅱ);有降水积云(Ⅲ)和其它(Ⅳ)4类,并按照通道间相关性最小和与大气水平能见度相关最大的原则进行通道筛选。初步分析表明,水平能见度确实与MODIS卫星表观反照率存在一定的函数关系。在此基础上,对每一类进行通道筛选和特征因子提取,建立分类的反演经验模型进行大气水平能见度的遥感反演,并与观测结果进行了比较。结果表明,在Ⅰ和Ⅲ类大气状况条件下,能见度的反演结果较好(相关系数分别达到0.6和0.9,远大于95%信度检验水平),而大气状况较为复杂的II类反演精度较低,相关系数只有0.5(仅达94.2%显著性检验水平)。由此可见,对于海上下垫面均一、而大气状况复杂的遥感反演问题,根据大气状况分类简化的统计反演是较为有效的1种方法。同时,对大气的辐射消光特性的了解和MODIS通道特性的分析,也是决定反演结果的重要因素。     

基于MODIS影像的鄱阳湖湖面积与水位关系研究

利用统计分析的方法,根据2001年获取的13景鄱阳湖区无云MODIS影像中的9景提取的水体面积,并结合同步观测的水文数据分别采用线性、对数和指数3种模型模拟湖面积-水位之间关系。结果显示对数模型相关性最好（R²=0.918）,其次为线性和指数模型。利用另外4景MODIS影像对模型进行检验表明,该模型精度较高,模拟的最大误差为3.36%。本研究显示,可根据鄱阳湖水位观测值,利用该模型预测鄱阳湖洪涝期洪水淹没面积,以弥补云天状况下光学遥感难以监测到洪水淹没范围的不足。本研究为利用遥感影像实时监控鄱阳湖水情空间动态变化提供了可行的方法,对湖泊、水库的泛洪监测、调洪功能分析具有重要意义。     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.     

基于MODIS数据的雪面温度遥感反演(英文)

On the basis of simplification of the Planck function in a low temperature range, this paper revises the practical split-window algorithm and presents a method for retrieving snow surface temperature （Ts） based on MODIS data in the middle-latitude region. The application of this method in Qinghai Lake region reveals that it is feasible for the retrieval of Ts. Results of correlation analysis indicate that there was strong negative relationship between Ts and altitude. By analyzing three typical areas in which land cover was relatively homogenous, this paper discusses the relationship between Ts and normalized difference snow index （NDSI） and then presents a new concept named ＂NDSI-Ts space＂.